Bilkent university at TRECVID 2006

We describe our third participation, that includes one high-level feature extraction run, and two manual and one interactive search runs, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual and textual information were used where visual information consisted of color, texture and edge-based low-level features and textual information consisted of the speech transcript provided in the collection

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Use of early conception factor test for determining pregnancy and embryonic mortality status of dairy cows

The aim of this study was to evaluate the accuracy of the ECF test for detecting the pregnancy status and embryonic mortality and to compare the reliability of ECF test from among ultrasonography and serum progesterone level. In this study, two groups were designed: the study group (n = 15) and control group (n = 9). All cows were observed for estrus activity four times daily. Cows in the study group were inseminated. After insemination, at the 7, 20, 30 and 45th days ECF test and ultrasonographic examination were applied to check the pregnancy status. Cows in the control group were not inseminated and examination procedure was performed like in the study group. Twenty days after insemination, pregnant positive cows that had been determined by ultrasonography were designated the study group. Twenty days after insemination, ECF test were applied and progesterone levels were determined in the serum samples obtained from pregnant positive cows. Fifteen cows in the study group were checked 20 days after insemination and determined pregnant. Their pregnancy status was confirmed 20 days after insemination by using ultrasonography. In the 30 th and 45th days ultrasonography was repeated, after which 13 cows were determined pregnant. In the serum of these two cows progesterone levels fell under 2 ng/ml. However, in the 20th day these cows' progesterone levels was higher than 2 ng/ml, in two cows embryonic death occurred. In cows which were determined as pregnant by ultrasonography at the 20th day, the ECF test was applied at the 7th day and 10 cows from this group had a positive reaction (66.7%). Test specificity, PPV and NPV results were 44.4%, 66.7% and 44.4% respectively; at the 20th day the ECF test was positive for 9 cows (60%), specificity, PPV and NPV results were 33.3%, 60.0% and 33.3%; at the 30th day, the ECF test was positive for 12 cows (92.3%), test specificity, PPV and NPV results were 45.5%, 66.7% and 83.3%; at the 45th day, 10 cows (76.9%), test specificity, PPV and NPV results were 54.5%, 66.7% and 66.7% respectively. Between the study groups, the ECF test accuracy at the 7th and 20th days were found lower than at the other days. The test's accuracy was determined the highest at the 30th day (70.8%), and the lowest at the 20th day (50%). The results show that ECF test is an unreliable method for pregnancy diagnosis and for determining embryonic death in dairy cows and these data indicate that the current ECF test cannot accurately identify the nonpregnant cows

Effect of somatic cell count on milk yield and composition of first and second lactation dairy cows

WOS: 000351716800014This study was carried out to investigate the effect of somatic cell count (SCC) on milk yield and milk composition in first and second lactation Holstein dairy cows. Thirty cows in first lactation and 49 cows in second lactation were used in the study. Animals were 15 +/- 9.87 days in milk. Individual milk samples were collected monthly from June 2009 to March 2010, and somatic cell counts, milk protein, milk fat, lactose and milk urea-N were determined. Four SCC groups were formed for determining effect of SCC on milk yield and composition. These groups were as follows: = 1.000x10(3) cell/mL. It was observed that SCC had a high significant effect on milk yield, milk protein, milk lactose (P0.05). This study indicates that high SCC negatively affects not only milk yield but also milk composition and quality.Department of Scientific Research Projects, Nigde University [99/14]the authors would like to thank the Department of Scientific Research Projects, Nigde University, for funding the project (Project Number: 99/14) and the Ek-Ta Meat and Milk Products Company for animal support